Coronavirus disease 2019 (COVID-19) is an infectious disease caused by a new type of coronavirus: severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak first started in Wuhan, China in December 2019. The first kown case of COVID-19 in the U.S. was confirmed on January 20, 2020, in a 35-year-old man who teturned to Washington State on January 15 after traveling to Wuhan. Starting around the end of Feburary, evidence emerge for community spread in the US.
We, as all of us, are indebted to the heros who fight COVID-19 across the whole world in different ways. For this data exploration, I am grateful to many data science groups who have collected detailed COVID-19 outbreak data, including the number of tests, confirmed cases, and deaths, across countries/regions, states/provnices (administrative division level 1, or admin1), and counties (admin2). Specifically, I used the data from these three resources:
JHU (https://coronavirus.jhu.edu/)
The Center for Systems Science and Engineering (CSSE) at John Hopkins University.
World-wide counts of coronavirus cases, deaths, and recovered ones.
NY Times (https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html)
The New York Times
``cumulative counts of coronavirus cases in the United States, at the state and county level, over time’’
COVID Trackng (https://covidtracking.com/)
COVID Tracking Project
``collects information from 50 US states, the District of Columbia, and 5 other US territories to provide the most comprehensive testing data’’
Assume you have cloned the JHU Github repository on your local machine at ``../COVID-19’’.
The time series provide counts (e.g., confirmed cases, deaths) starting from Jan 22nd, 2020 for 253 locations. Currently there is no data of individual US state in these time series data files.
Here is the list of 10 records with the largest number of cases or deaths on the most recent date.
Next, I check for each country/region, what is the number of new cases/deaths? This data is important to understand what is the trend under different situations, e.g., population density, social distance policies etc. Here I checked the top 10 countries/regions with the highest number of deaths.
The raw data from Hopkins are in the format of daily reports with one file per day. More recent files (since March 22nd) inlcude information from individual states of US or individual counties, as shown in the following figure. So I turn to NY Times data for informatoin of individual states or counties.
The data from NY Times are saved in two text files, one for state level information and the other one for county level information.
The currente date is
## [1] "2020-05-07"
First check the 30 states with the largest number of deaths.
## date state fips cases deaths
## 3623 2020-05-07 New York 36 332931 26206
## 3621 2020-05-07 New Jersey 34 133635 8801
## 3612 2020-05-07 Massachusetts 25 73721 4552
## 3613 2020-05-07 Michigan 26 45643 4343
## 3630 2020-05-07 Pennsylvania 42 56149 3599
## 3604 2020-05-07 Illinois 17 70802 3139
## 3596 2020-05-07 Connecticut 9 31784 2797
## 3594 2020-05-07 California 6 62481 2561
## 3609 2020-05-07 Louisiana 22 30652 2135
## 3599 2020-05-07 Florida 12 38820 1599
## 3611 2020-05-07 Maryland 24 29476 1503
## 3605 2020-05-07 Indiana 18 22942 1414
## 3600 2020-05-07 Georgia 13 30524 1333
## 3627 2020-05-07 Ohio 39 22131 1271
## 3636 2020-05-07 Texas 48 36682 1016
## 3595 2020-05-07 Colorado 8 18264 942
## 3641 2020-05-07 Washington 53 17334 903
## 3640 2020-05-07 Virginia 51 21570 769
## 3624 2020-05-07 North Carolina 37 13431 521
## 3614 2020-05-07 Minnesota 27 9365 508
## 3592 2020-05-07 Arizona 4 9945 450
## 3616 2020-05-07 Missouri 29 9410 449
## 3615 2020-05-07 Mississippi 28 8686 396
## 3632 2020-05-07 Rhode Island 44 10530 388
## 3643 2020-05-07 Wisconsin 55 9215 374
## 3590 2020-05-07 Alabama 1 9046 369
## 3633 2020-05-07 South Carolina 45 7142 316
## 3608 2020-05-07 Kentucky 21 6173 302
## 3619 2020-05-07 Nevada 32 5888 293
## 3598 2020-05-07 District of Columbia 11 5654 285
For these 20 states, I check the number of new cases and the number of new deaths. Part of the reason for such checking is to identify whether there is any similarity on such patterns. For example, could you use the pattern seen from Italy to predict what happen in an individual state, and what are the similarities and differences across states.
Next I check the relation between the cumulative number of cases and deaths for these 10 states, starting on March
First check the 30 counties with the largest number of deaths.
## date county state fips cases deaths
## 122759 2020-05-07 New York City New York NA 185653 19141
## 122758 2020-05-07 Nassau New York 36059 37593 2340
## 121624 2020-05-07 Cook Illinois 17031 48341 2110
## 122289 2020-05-07 Wayne Michigan 26163 17667 2012
## 122778 2020-05-07 Suffolk New York 36103 35892 1599
## 121231 2020-05-07 Los Angeles California 6037 29427 1418
## 122684 2020-05-07 Essex New Jersey 34013 15095 1381
## 122679 2020-05-07 Bergen New Jersey 34003 16609 1319
## 122786 2020-05-07 Westchester New York 36119 30709 1305
## 122204 2020-05-07 Middlesex Massachusetts 25017 16676 1103
## 121329 2020-05-07 Fairfield Connecticut 9001 12679 977
## 122686 2020-05-07 Hudson New Jersey 34017 16354 923
## 121330 2020-05-07 Hartford Connecticut 9003 6750 867
## 122697 2020-05-07 Union New Jersey 34039 13781 829
## 123170 2020-05-07 Philadelphia Pennsylvania 42101 17047 816
## 122270 2020-05-07 Oakland Michigan 26125 7624 789
## 122689 2020-05-07 Middlesex New Jersey 34023 13411 737
## 122693 2020-05-07 Passaic New Jersey 34031 14133 703
## 122257 2020-05-07 Macomb Michigan 26099 5876 678
## 122208 2020-05-07 Suffolk Massachusetts 25025 14732 663
## 121333 2020-05-07 New Haven Connecticut 9009 8678 643
## 122206 2020-05-07 Norfolk Massachusetts 25021 6729 608
## 122200 2020-05-07 Essex Massachusetts 25009 10610 578
## 123165 2020-05-07 Montgomery Pennsylvania 42091 4915 506
## 122691 2020-05-07 Morris New Jersey 34027 5702 503
## 122692 2020-05-07 Ocean New Jersey 34029 7209 500
## 123785 2020-05-07 King Washington 53033 7182 482
## 122124 2020-05-07 Orleans Louisiana 22071 6626 463
## 121385 2020-05-07 Miami-Dade Florida 12086 13584 454
## 122202 2020-05-07 Hampden Massachusetts 25013 4441 434
For these 30 counties, I check the number of new cases and the number of new deaths.
The positive rates of testing can be an indicator on how much the COVID-19 has spread. However, they are more noisy data since the negative testing resutls are often not reported and the tests are almost surely taken on a non-representative random sample of the population. The COVID traking project proides a grade per state: ``If you are calculating positive rates, it should only be with states that have an A grade. And be careful going back in time because almost all the states have changed their level of reporting at different times.’’ (https://covidtracking.com/about-tracker/). The data are also availalbe for both counties and states, here I only look at state level data.
Since the daily postive rate can fluctuate a lot, here I only illustrae the cumulative positave rate across time, for four states with grade A data. Of course since this is an R markdown file, you can modify the source code and check for other states.
## R version 3.6.2 (2019-12-12)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.4
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## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
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## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## other attached packages:
## [1] httr_1.4.1 ggpubr_0.2.5 magrittr_1.5 ggplot2_3.2.1
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## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.3 pillar_1.4.3 compiler_3.6.2 tools_3.6.2
## [5] digest_0.6.23 evaluate_0.14 lifecycle_0.1.0 tibble_2.1.3
## [9] gtable_0.3.0 pkgconfig_2.0.3 rlang_0.4.4 yaml_2.2.1
## [13] xfun_0.12 gridExtra_2.3 withr_2.1.2 dplyr_0.8.4
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